ENH: add a result_type test with python scalars

Author: ev-br

Diff for: array_api_tests/hypothesis_helpers.py

@@ -10,11 +10,7 @@
 from hypothesis import assume, reject
 from hypothesis.strategies import (SearchStrategy, booleans, composite, floats,
                                    integers, complex_numbers, just, lists, none, one_of,
-<<<<<<< HEAD
-                                   sampled_from, shared, builds, nothing)
-=======
                                    sampled_from, shared, builds, nothing, permutations)
->>>>>>> ENH: add a test that result_type does not depend on the order of arguments
 
 from . import _array_module as xp, api_version
 from . import array_helpers as ah

Diff for: array_api_tests/test_data_type_functions.py

@@ -208,6 +208,7 @@ def test_isdtype(dtype, kind):
     assert out == expected, f"{out=}, but should be {expected} [isdtype()]"
 
 
+@pytest.mark.min_version("2024.12")
 class TestResultType:
     @given(dtypes=hh.mutually_promotable_dtypes(None))
     def test_result_type(self, dtypes):
@@ -230,3 +231,30 @@ def test_arrays_and_dtypes(self, pair, data):
         out = xp.result_type(*a_and_dt)
         ph.assert_dtype("result_type", in_dtype=s1+s2, out_dtype=out, repr_name="out")
 
+    @given(dtypes=hh.mutually_promotable_dtypes(2), data=st.data())
+    def test_with_scalars(self, dtypes, data):
+        out = xp.result_type(*dtypes)
+
+        if out == xp.bool:
+            scalars = [True]
+        elif out in dh.all_int_dtypes:
+            scalars = [1]
+        elif out in dh.real_dtypes:
+            scalars = [1, 1.0]
+        elif out in dh.numeric_dtypes:
+            scalars = [1, 1.0, 1j]        # numeric_types - real_types == complex_types
+        else:
+            raise ValueError(f"unknown dtype {out = }.")
+
+        scalar = data.draw(st.sampled_from(scalars))
+        inputs = data.draw(st.permutations(dtypes + (scalar,)))
+
+        out_scalar = xp.result_type(*inputs)
+        assert out_scalar == out
+
+        # retry with arrays
+        arrays = tuple(xp.empty(1, dtype=dt) for dt in dtypes)
+        inputs = data.draw(st.permutations(arrays + (scalar,)))
+        out_scalar = xp.result_type(*inputs)
+        assert out_scalar == out
+